In order to validate our hypotheses that a domain-agnostic structured latent space helps to stabilize \(p(y|\mathbf {z})\)  and generalize to new domains, we conduct an experiment as a proof of concept. Fig. REF  presents a comparison for the standard domain generalization (DG) setting on the PACS dataset [1]} using ResNet-18 backbone.
We see that structuring the latent space (blue bars) provides performance gains on all domains and enhances the average gain, compared to employing multimodal alignment alone (orange bars). The highest gain is achieved for the most difficult sketch domain that has a large domain shift from the source domains (photo, art, cartoon), demonstrating the advantage of our domain-agnostic partitioning.
